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A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques

A financial market is a platform to produce data streams continuously and around 1. 145 Trillion MB of data per day. Estimation and the analysis of unknown or dynamic behaviors of these systems is one the challenging tasks. Analysis of these systems is very much essential to strengthen the environme...

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Autores principales: M. S., Abdul Razak, Nirmala, C. R., Aljohani, Maha, Sreenivasa, B. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471369/
https://www.ncbi.nlm.nih.gov/pubmed/36117781
http://dx.doi.org/10.3389/frai.2022.950659
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author M. S., Abdul Razak
Nirmala, C. R.
Aljohani, Maha
Sreenivasa, B. R.
author_facet M. S., Abdul Razak
Nirmala, C. R.
Aljohani, Maha
Sreenivasa, B. R.
author_sort M. S., Abdul Razak
collection PubMed
description A financial market is a platform to produce data streams continuously and around 1. 145 Trillion MB of data per day. Estimation and the analysis of unknown or dynamic behaviors of these systems is one the challenging tasks. Analysis of these systems is very much essential to strengthen the environmental parameters to stabilize society activities. This can elevate the living style of society to the next level. In this connection, the proposed paper is trying to accommodate the financial data stream using the sliding window approach and random forest algorithm to provide a solution to handle concept drift in the financial market to stabilize the behavior of the system through drift estimation. The proposed approach provides promising results in terms of accuracy in detecting concept drift over the state of existing drift detection methods like one class drifts detection (OCDD), Adaptive Windowing ADWIN), and the Page-Hinckley test.
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spelling pubmed-94713692022-09-15 A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques M. S., Abdul Razak Nirmala, C. R. Aljohani, Maha Sreenivasa, B. R. Front Artif Intell Artificial Intelligence A financial market is a platform to produce data streams continuously and around 1. 145 Trillion MB of data per day. Estimation and the analysis of unknown or dynamic behaviors of these systems is one the challenging tasks. Analysis of these systems is very much essential to strengthen the environmental parameters to stabilize society activities. This can elevate the living style of society to the next level. In this connection, the proposed paper is trying to accommodate the financial data stream using the sliding window approach and random forest algorithm to provide a solution to handle concept drift in the financial market to stabilize the behavior of the system through drift estimation. The proposed approach provides promising results in terms of accuracy in detecting concept drift over the state of existing drift detection methods like one class drifts detection (OCDD), Adaptive Windowing ADWIN), and the Page-Hinckley test. Frontiers Media S.A. 2022-08-31 /pmc/articles/PMC9471369/ /pubmed/36117781 http://dx.doi.org/10.3389/frai.2022.950659 Text en Copyright © 2022 M. S., Nirmala, Aljohani and Sreenivasa. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
M. S., Abdul Razak
Nirmala, C. R.
Aljohani, Maha
Sreenivasa, B. R.
A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques
title A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques
title_full A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques
title_fullStr A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques
title_full_unstemmed A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques
title_short A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques
title_sort novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471369/
https://www.ncbi.nlm.nih.gov/pubmed/36117781
http://dx.doi.org/10.3389/frai.2022.950659
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